Recommender system construction using latent semantic analysis and data mining methods one-commerce data

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2019
Özer, Arif Görkem
Recommender systems are developed to provide better recommendations to users of e-commerce applications. In addition to this goal, e-commerce applications benefit from their recommender systems to show advertisements to users, apply discounts on specific items. The task of recommending an item to a user is always a challenge; luckily, there are many methods developed to complete this task such as collaborative filtering, association rule mining etc. These methods mainly look at the co-occurrence of items; however, we think that user behavior on different items should be extracted by doing latent semantic analysis on the data. Latent semantic analysis is used for understanding the context of a text, we think that it can be used for providing recommendations by processing transactional data. The data used throughout this thesis work consists of transactions made in various e-commerce companies. In this thesis work, existing methods and proposed recommendation methods are examined and recommendation results on this data are shown.
Citation Formats
A. G. Özer, “Recommender system construction using latent semantic analysis and data mining methods one-commerce data,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Computer Engineering., 2019.